Kernel Conditional Random Fields: Representation and Clique Selection

Kernel Conditional Random Fields: Representation and Clique Selection (2004)

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by John Lafferty Lafferty , Xiaojin Zhu , Yan Liu

Venue:	in ICML
Citations:	53 - 4 self

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@INPROCEEDINGS{Lafferty04kernelconditional,
    author = {John Lafferty Lafferty and Xiaojin Zhu and Yan Liu},
    title = {Kernel Conditional Random Fields: Representation and Clique Selection},
    booktitle = {in ICML},
    year = {2004}
}

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Abstract

Kernel conditional random fields (KCRFs) are introduced as a framework for discriminative modeling of graph-structured data. A representer theorem for conditional graphical models is given which shows how kernel conditional random fields arise from risk minimization procedures defined using Mercer kernels on labeled graphs. A procedure for greedily selecting cliques in the dual representation is then proposed, which allows sparse representations. By incorporating kernels and implicit feature spaces into conditional graphical models, the framework enables semi-supervised learning algorithms for structured data through the use of graph kernels.

Citations

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